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167 results for “greenhouse gas”
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
N2O raw data from static greenhouse gas chamber measurements
<p>This dataset contains N<sub>2</sub>O concentration measurements of a 2 years measurement campaign for greenhouse gas fluxes from agricultural soils.</p> <p>The format of the data is ready to be fed into the gasfluxes R package on CRAN to calculate fluxes for each individual chamber measurement (identical IDs are referred to one single measurement, the ID contains the measurement day, treatment and replicate).</p> <p>The data is originally published in Krauss et al. 2017 and further used for improvements of the flux calculation procedure in Hüppi et a. 2018 (see references)</p>
Greenhouse gas emissions (lifecycle) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The CO_2 emissions for these vehicles were: </p> <ul> <li> <p>for the model Tesla 3, CO_2 emissions were 161.8 gCO_2 eq/mile, including 31.8 gCO_2 eq/mile in vehicle production, 25 gCO_2 eq/mile in battery production, and 105 gCO_2 eq/mile in electricity production.</p> </li> <li> <p>for the model Infiniti Q50, CO_2 emissions were 503.8 gCO_2 eq/mile , including vehicle production 40.5 gCO_2 eq/mile, fuel production 91.3 gCO_2 eq/mile, in-service combustion 372 gCO_2 eq/mile.</p> </li> </ul>
Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households
<p><strong>Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households</strong></p> <p> </p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p>
Regional greenhouse gas net emission intensities by land cover category in Finland
<p>The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing.</p> <p><strong>Data description</strong></p> <p><em>Data format.</em> The data are distributed as GeoTiff raster files, which can be read using most GIS-software.</p> <p><em>Units and definitions</em>. The land cover net emission intensities are shared as raster data with a 250m-by-250m resolution in the ETRS-TM35FIN projected coordinate system. Negative values correspond to sinks (only sinks of C/CO<sub>2</sub> considered). The emission intensities are reported as total emission intensities in carbon dioxide equivalents (gCO<sub>2</sub>-eq m<sup>-2</sup>) based on the 100-year global warming potential as reported in the IPCC 5<sup>th</sup> assessment report (Myhre et al. 2013, p. 73). All cells which do not include emissions from the corresponding land use are classified as <code class="language-sql">NULL</code>s or <em>no data</em>, which should be taken into account if combining raster layers. Where the source data report emission coefficients in the amount of the main element (e.g. C for CO<sub>2</sub> or N for N<sub>2</sub>O) they have been converted to the amounts of the corresponding gas using the standard atomic weights of the relevant atoms (C: 12.011, O: 15.999, N: 14.007, H: 1.008) before conversion to carbon dioxide equivalents. See the related publication for the values of the emission coefficients used and further methodological details (Holmberg et al. 2023).</p> <p><em>Data processing</em>. Data processing for the production of the 250m-by-250m emission intensity raster maps was conducted using GRASS GIS 8.2 (GRASS Development Team, 2022).</p> <p>Land cover emissions derived from vector data (rivers, lakes, agricultural land) were rasterized at a resolution of 1m<sup>2</sup> with the emission intensity as the raster cell value. For rivers, linear features representing rivers having a width of 2 to 5 meters were converted first to areal features by creating a buffer of 1.75 meters to represent an average width of 3.5 meters (see <em>Rivers</em> below). The buffer was created <em>without caps</em> so that the total length of the linear segments was not changed. The buffered river features were merged with the areal features removing the potentially overlapping parts.</p> <p>For all source raster data, the data were available at a 16m-by-16m meter resolution. Emission intensities were aggregated to 250m-by-250m by first summing over the original raster cells intersecting with each aggregate cell while accounting for the proportion of each cell overlapping with the aggregated cell and then multiplying by the area of the original cell. The resulting raster values were divided by the total area of the aggregate cell to acquire average emission intensities. Hence, rasters including a lower proportion of the corresponding land use have lower emission intensities.</p> <p><strong>Thematic layers</strong></p> <p><em>Cropland</em>. CO<sub>2</sub> emissions from cropland were estimated for mineral soils and organic soils separately using emission coefficients from the national greenhouse gas inventory report for 2023. Averaged emission coefficients for the years 2010–2020 for southern and northern Finland were used for mineral soils (Statistics Finland 2023b, Table 3_App_6j). For organic soils separate emission coefficients were used for annual and perennial crops (IPCC 2014, Table 2.1). Cropland and crop data were acquired from the Finnish Food Authority’s Land parcel register for year 2020. Soils were classified into mineral and organic soils by intersecting the field parcels with the soil body layer of the Finnish soil database (Lilja et al. 2006, Lilja et al. 2017).</p> <p>Data files:</p> <ul> <li>Net missions from cropland on mineral soils: <code>cropland_mineral_250m_250m_mean.tif</code></li> <li>Net missions from cropland on organic soils: <code>cropland_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Forests</em>. The net emissions from forests are estimated as the balance of carbon sequestration due to gross primary production of trees and understory vegetation and carbon loss due to harvested biomass, and emission from decomposition of harvest residues, litter, and soil organic matter. Forest productivity is modelled using the process-based forest growth model PREBAS (Minunno et al. 2016, 2019, Junttila et al. 2023, Mäkelä et al. 2023). The initial state for the forest model for the three main forestry species Scots pine, Norway spruce, and Silver birch is derived from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2015</a>) and harvesting intensities are modelled on the basis of the Finnish national statistics (National Resources Institute Finland 2023). The PREBAS forest net emissions represent annual averages for the period 2017–2025.</p> <p>CO<sub>2</sub> emissions from decomposition on mineral soils are estimated with the soil carbon model YASSO07 (Liski et al. 2005, Tuomi et al. 2009). On drained peatlands, in addition to CO<sub>2</sub> emissions due to peat and litter decomposition, the soil emissions include the CH<sub>4</sub> and N<sub>2</sub>O emissions. The net emissions due to CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from drained peatland (Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023) are calculated using emission coefficients for nutrient rich sites (herb-rich and blueberry type), and nutrient poor sites (lingonberry, dwarf-shrub, and lichen type).</p> <p>Data files:</p> <ul> <li>Net missions from forest on mineral soils: <code>forest_mineral_250m_250m_mean.tif</code></li> <li>Net missions from forest on organic soils: <code>forest_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Lakes</em>. Emissions of CO<sub>2</sub> and CH<sub>4</sub> were estimated for lakes using size dependent emission coefficients. The lakes were classified into five size classes with emission coefficients for CO<sub>2</sub> evasion (Kortelainen et al. 2006), CH<sub>4</sub> diffusion (Juutinen et al. 2009) and ebullition (Bastviken et al. 2004) as well as the CH<sub>4</sub> emissions due to the macrophytes <em>Phragmites australis</em> and <em>Equisetum fluviatile</em> (Juutinen et al. 2003, Bergström et al. 2007, 2011). The lake date was from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute.</p> <p>Data files:</p> <ul> <li>Net emissions from lakes: <code>lakes_250m_250m_mean.tif</code></li> </ul> <p><em>Rivers</em>. CO<sub>2</sub> emissions from rivers were estimated using emission coefficients based on the width of the stream. The width dependent emission coefficients were derived from stream order specific emission coefficients of Swedish rivers (Humborg et al. 2010) by classifying the rivers into width groups and with the emission coefficients chosen based on the stream order specific coefficient of corresponding average width. The river emissions were calculated from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute which represents rivers wider than 5 m as areal features, and rivers < 5 m wide as linear features. For rivers < 5m wide, an average width of 3.5 m was assumed.</p> <p>Data files:</p> <ul> <li>Net emssions from rivers: <code>rivers_250m_250m_mean.tif</code></li> </ul> <p><em>Undrained mires</em>. Total net emissions were estimated for undrained mires in Finland using average emission coefficients for CH<sub>4</sub> (Minkkinen and Ojanen 2013), CO<sub>2</sub> (Sallantaus 1994 , Turunen et al. 2002), and N<sub>2</sub>O (Minkkinen et al. 2020). The emission coefficients represent the long term accumulation of carbon as well as the emission of CH<sub>4</sub> and from N<sub>2</sub>O peatland. Peatland sites were extracted from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2019</a>; see also Mäkisara et al. 2022) and undrained mires were delineated using data provided by the Natural Resources Institute Finland. The undrained mires were classified into four classes using the MS-NFI data: 1) productive forested mires, 2) sedge fens, 3) other open and sparsely treed fens and 4) ombrotrophic bogs, which mainly differ in their emission coefficients for methane (Minkkinen and Ojanen 2013).</p> <p>Data files:</p> <ul> <li>Net missions from undrained mires: <code>undrained_mires_250m_250m_mean.tif</code></li> </ul>
Machine learning methods for gap-filling in greenhouse gas emissions databases
<p>Datasets for use with code related to "Machine learning methods for gap-filling in greenhouse gas emissions databases" manuscript submitted to the Journal of Industrial Ecology. Code for using the datasets can be found at <a href="https://github.com/luke-scot/ml-ghg-databases">https://github.com/luke-scot/ml-ghg-databases</a>.</p>
Greenhouse Gas and Water Chemistry Data from Ponds in the Twin-Cities area of Minnesota, 2021
Freshwaters are significant contributors of greenhouse gases to the atmosphere, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Small waterbodies such as ponds are now recognized to have disproportionate greenhouse gas emissions relative to their size, but recorded emissions from ponds have varied by several orders of magnitude. To assess drivers of variation in pond greenhouse gas dynamics, this study measured concentrations and emissions of CO2, CH4, and N2O across 26 ponds in Minnesota, USA during the ice-free season. The studied ponds ranged in land-use, from urban stormwater ponds to natural forested ponds. Water chemistry variables were measured with sonde profiles as well as surface water samples. Greenhouse gas emsisions of CO2 and CH4 were measured with a floating chamber at three locations on each pond, and gas concentrations of CO2, CH4, and N2O were measured using a headspace equilibrium technique in both the surface waters and bottom waters of each pond.
Greenhouse gas data of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, 2016-2018
This dataset is a collation of 3-year direct measurement values of CO2, CH4 and N2O concentrations and fluxes of permafrost-affected reservoirs on the Qinghai-Tibet Plateau, along with information on location and chemical conditions of the study sites. Given the rarity and high value of this data set, it will be very valuable for the next update to global reservoir GHG flux estimates.
Dataset for: Common carp (Cyprinus carpio) invasion alters greenhouse gas emissions in shallow lakes.
Climate change and invasive species are among the most important environmental problems of this century. Freshwaters are important regulators of the global carbon cycle and a key source of atmospheric greenhouse gases. However, freshwater environments may be particularly susceptible to species invasion and adverse effects, and the consequences of altered species assemblages on greenhouse gas emissions remain poorly understood. In this study, we analyzed the impact of one of the world's most damaging invasive species, the common carp, on freshwater greenhouse gas emissions. We show that lakes with invasive carp had lower methane emissions despite increased eutrophication, contradicting the well-established assumption that methane emissions from lakes increase with nutrient levels and productivity. This is likely due to substantial depletion of the benthic environment. As invasive species spread continues, new species assemblages may therefore disrupt ecosystem functioning and diverse global cycles in unexpected ways.
European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data
<p>The files uploaded under this doi number represent the updated data sets used in the manuscript submitted to ESSDD in its revised version entitled: "European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data" Excel files including the data behind all the manuscript figures are available for download only for review purposes. We added, as sugggested by the referees, metadata belonging to UNFCCC 2018, FAOSTAT, EDGAR v4.3.2, CAPRI and CBM.</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
<p><span>Earthworms are known to stimulate soil greenhouse gas (GHG) emissions, but the majority of previous studies have used simplified model systems or lacked continuous high-frequency measurements. To address this, we conducted a two-year study using large lysimeters (</span><span>5 m<sup>2</sup> area and 1.5 m soil depth) </span><span>in an ecotron facility, continuously measuring ecosystem-level CO<sub>2</sub>, N<sub>2</sub>O, and H<sub>2</sub>O fluxes. We investigated the impact of endogeic and anecic earthworms on GHG emissions and ecosystem water use efficiency (WUE) in a simulated agricultural setting. Although we observed transient stimulations of carbon fluxes in the presence of earthworms, cumulative fluxes over the study indicated no significant increase in CO<sub>2</sub> emissions. Endogeic earthworms reduced N<sub>2</sub>O emissions during the wheat culture (-44.6%), but this effect was not sustained throughout the experiment. No consistent effects on ecosystem evapotranspiration or WUE were found. Our study suggests that earthworms do not significantly contribute to GHG emissions over a two-year period in experimental conditions that mimic an agricultural setting. These findings highlight the need for realistic experiments and continuous GHG measurements.</span></p>
Greenhouse gas dynamics in river networks fragmented by drying and damming
<p>River fragmentation by drying and damming is occurring more frequently in the Anthropocene, yet there is limited research about its effects on greenhouse gas (GHG) fluxes. These fragmented rivers have the potential to be important sources of GHGs to the atmosphere through both similar and dissimilar mechanisms. Our objective was to review the literature of the individual and the interactive effects of fragmentation by drying and damming on GHG fluxes in river networks, identifying the magnitudes and drivers of CO<sub>2</sub>, methane (CH<sub>4</sub>), and N<sub>2</sub>O flux rates. We conducted a systematic search of studies addressing the separate and interactive effects of drying and damming on GHG fluxes from running waters. The search was primarily conducted using Web of Science for studies published from 1900 to January 2021. For the 42 studies about rivers impacted by drying, 54 about damming, and 6 about their interactive effects, we collected a suite of qualitative and quantitative information. The major proximal drivers of GHG emissions in river networks impacted by drying were sediment moisture, sediment temperature, sediment organic matter content and sediment texture. In networks impacted by damming, the major proximal drivers were water temperature, dissolved oxygen, and chlorophyll ɑ. We found research lacking in non-arid climates for drying, and on small water retention structures for damming. We propose a conceptual model where the spatial distribution of fragmentation is the principle driver of GHG fluxes at the network scale. </p>
Data from: Promoting success in thin layer sediment placement: effects of sediment grain size and amendments on salt marsh plant growth and greenhouse gas exchange
<p>Thin layer sediment placement (TLP) is a method to mitigate factors resulting in loss of elevation and severe alteration of hydrology, such as sea level rise and anthropogenic modifications, and prolong the lifespan of drowning salt marshes. However, TLP success may vary due to plant stress associated with reductions in nutrient availability and hydrologic flushing or through the creation of acid sulfate soils. This study examined the influence of sediment grain size and soil amendments on plant growth, soil and porewater characteristics, and greenhouse gas exchange for three key US salt marsh plants: <em>Spartina alterniflora, Spartina patens, </em>and <em>Salicornia pacifica. </em>We found that bioavailable nitrogen concentrations (measured as extractable NH<sub>4</sub><sup>+</sup>-N) and porewater pH and salinity were found to have an inverse relationship with grain size, while soil redox was more reducing in finer sediments. This suggests that utilizing finer sediments in TLP projects will result in a more reduced environment with higher nutrient availability, while larger grain-sized sediments will be better flushed and oxidized. We further found that grain size had a significant effect on vegetation biomass allocation and rates of gas exchange, although these effects were species-specific. We found that soil amendments (biochar and compost) did not subsidize plant growth but were associated with increases in soil respiration and methane emissions. Biochar amendments were additionally ineffective in ameliorating acid sulfate conditions. This study uncovers complex interactions between sediment type and vegetation, emphasizing limitations of soil amendments. The findings aid restoration project managers in making informed decisions regarding sediment type, target vegetation, and soil amendments for successful TLP projects.</p>
Structural breaks in greenhouse gas emissions for OECD countries in 1995-2022 and 37 sectors
<p>The quantitative assessment of policy instruments aimed at climate change mitigation requires the rigorous identification of abnormal changes in greenhouse gas emissions. We present a new dataset of robust level changes in greenhouse gas emissions that cannot be explained by aggregate socioeconomic fluctuations. Modern methods of structural break identification based on two-way fixed effects models are employed to estimate the size of significant level changes in emissions. The resulting dataset spans information for all OECD countries and all 37 IPCC sectors, ranging from 1995 to 2022. The data unveils large differences in abnormal changes in emissions across gases, countries and sectors, as well as over time. Our resulting data can be applied to a broad range of research questions, including the analysis of the comparative efficacy of policy instruments to mitigate climate change.</p>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
<p><strong>Description</strong></p> <p>This dataset contains remote sensing data from the ESA Copernicus missions Sentinel-2 and Sentinel-5P (tropsopheric NO2 column density) in the 2018-2020 timespan. The satellite measurements each cover ~3100 locations in Europe and ~100 on the US Westcoast, each with a size of 1.2x1.2km. The locations are selected such that each measurement is centered at the location of an air quality measurement station on the ground (from the European Environment Agency or the US Environmental Protection Agency, measuring NO2). This makes it possible to analyze spatiotemporally aligned remote sensing and ground-based measurements.</p> <p> The 13 Sentinel-2 bands are upsampled (bilinear) to 10m resolution and cropped to 120x120 pixel. For some locations multiple Sentinel-2 images are available. The images are stored as binary numpy `.npy` files organized into directories based on their locations. </p> <p>The Sentinel-5P data was pre-processed by mapping the measurements from consecutive satellite overpasses onto a common rectangular grid of 0.05×0.05◦(∼5×5km) across Europe. To harmonize the Sentinel-2 (10m to 60m, upscaled to 10m) and Sentinel-5P (5×3.5km, rescaled to 5×5km) imaging resolutions, the Sentinel-5P data is linearly interpolated to 10m resolution and cropped to 120×120 pixel around the locations of interest. Additionally, all measurements with a QA flag (qa_value) below 75 were discarded, following ESA recommendations. The Sentinel-5P data are stored as `.netcdf` file, organized by location. For each location, three such files are available, containing averaged Sentinel-5P measurements at different temporal frequencies (2018-2020, quarterly, monthly).</p> <p>The <p>samples_{frequency}_{area}.csv</p> files provide a list of observations with the corresponding file paths to a (cloud-free) Sentinel-2 image, the Sentinel-5P measurement, and the average NO2 concentration measurement by the EEA or EPA ground station. These files can be used for easy data-loading.</p> <p><strong>Content</strong></p> <p>The data is organized into the following files:</p> <ul> <li>README.md - this file</li> <li>sentinel-2-eea.tar.gz [33.1GB]</li> <li>sentinel-5p-eea.tar.gz [80.1GB]</li> <li>samples_2018_2020_eea.csv </li> <li>samples_quarterly_eea.csv</li> <li>samples_monthly_eea.csv</li> <li>sentinel-2-epa.tar.gz [0.15GB]</li> <li>sentinel-5p-epa.tar.gz [1.8GB]</li> <li>samples_2018_2020_epa.csv</li> <li>samples_quarterly_epa.csv</li> <li>samples_monthly_epa.csv</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Scheibenreif, L., Mommert, M., Borth, D., "</em>Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space<em>", Tackling Climate Change with Machine Learning workshop at ICML 2021.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2018-2020, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Linus Scheibenreif<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> linus.scheibenreif ( at ) unisg.ch</p>
Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw
<p>Data associated with publication "Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw". The data contained within this data set is arranged according to the main text and the supplementary information of this publication.</p> <p><strong>Background information</strong></p> <p>Field site: Stordalen mire, Abisko, Sweden (68 22ʹ N, 19 03ʹ E)</p> <p>Thaw stages: Palsa, bog and fen</p> <p>Type of samples: Gas samples, porewater samples, soil core samples</p>
Canadian fossil fuel production and greenhouse gas emissions compared to predictions following the 2.0°C scenario
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. McGlade and Ekins (2015) proposed quotas for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0°C by 2100. The proportion of each quota that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets already emitted within the 2010-2050 period. Emissions from five database are used in the calculations.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.